revenue management
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2022 ◽  
Author(s):  
David Simchi-Levi ◽  
Rui Sun ◽  
Huanan Zhang

We study in this paper a revenue-management problem with add-on discounts. The problem is motivated by the practice in the video game industry by which a retailer offers discounts on selected supportive products (e.g., video games) to customers who have also purchased the core products (e.g., video game consoles). We formulate this problem as an optimization problem to determine the prices of different products and the selection of products for add-on discounts. In the base model, we focus on an independent demand structure. To overcome the computational challenge of this optimization problem, we propose an efficient fully polynomial-time approximation scheme (FPTAS) algorithm that solves the problem approximately to any desired accuracy. Moreover, we consider the problem in the setting in which the retailer has no prior knowledge of the demand functions of different products. To solve this joint learning and optimization problem, we propose an upper confidence bound–based learning algorithm that uses the FPTAS optimization algorithm as a subroutine. We show that our learning algorithm can converge to the optimal algorithm that has access to the true demand functions, and the convergence rate is tight up to a certain logarithmic term. We further show that these results for the independent demand model can be extended to multinomial logit choice models. In addition, we conduct numerical experiments with the real-world transaction data we collect from a popular video gaming brand’s online store on Tmall.com. The experiment results illustrate our learning algorithm’s robust performance and fast convergence in various scenarios. We also compare our algorithm with the optimal policy that does not use any add-on discount. The comparison results show the advantages of using the add-on discount strategy in practice. This paper was accepted by J. George Shanthikumar, big data analytics.


2022 ◽  
Vol 155 ◽  
pp. 297-315
Author(s):  
Mitsuyoshi Fukushi ◽  
Felipe Delgado ◽  
Sebastián Raveau ◽  
Bruno F. Santos

2021 ◽  
Vol 15 (2) ◽  
pp. 27-37
Author(s):  
Roman Hlawiczka ◽  
Roman Blazek ◽  
Gabriel Santoro ◽  
Gianluca Zanellato

Research background: The article focuses on the issues of creative accounting, earnings management, and fraudulent accounting, which are global phenomena. These concepts are well known globally, as they are dealt with by many world-renowned authors. In this study, we applied bibliometric analysis to these concepts to reveal their interconnectedness. The research was conducted on a sample of more than 19,000 articles. Purpose of the article: The main goal of the study is to use the VosViewer design and visualisation program to capture and record the most common terms associated with the terms, ‘creative accounting’, ‘revenue management’, and ‘fraudulent accounting’, and to show a biometric network of the most commonly used terms. Methods: To capture and illustrate important words associated with the above terms, the VosViewer program was used, which drew mind maps that represented the words and expressions that were closest to the topic. Scientific articles from the Web of Science database, which contains many world-class articles related to the topic, were used as input data. Findings & Value added: The results of the study provided an interesting insight into the keywords associated with the issues of creative accounting, revenue management, and fraudulent accounting. The results show that the keywords and phrases are related, as several of them are repeated in each of the terms mentioned. This means that, although these terms are different in nature, they are nevertheless connected by many words and phrases. However, it remains necessary to observe that each of the given terms appears on a different colour of fraud (white, grey, or black fraud).


2021 ◽  
Vol 20 ◽  
pp. 100270
Author(s):  
Yongji Luo ◽  
Haifeng Yan ◽  
Shoushuai Zhang
Keyword(s):  

2021 ◽  
Vol 137 ◽  
pp. 336-344
Author(s):  
Giampaolo Viglia ◽  
Francesca De Canio ◽  
Anna Stoppani ◽  
Anna Chiara Invernizzi ◽  
Stefania Cerutti

Author(s):  
Christiane Barz ◽  
Simon Laumer ◽  
Marcel Freyschmidt ◽  
Jesús Martínez-Blanco

AbstractWe consider a real discrete pricing problem in network revenue management for FlixBus. We improve the company's current pricing policy by an intermediate optimization step using booking limits from standard deterministic linear programs. We pay special attention to computational efficiency. FlixBus' strategic decision to allow for low-cost refunds might encourage large group bookings early in the booking process. In this context, we discuss counter-intuitive findings comparing booking limits with static bid price policies. We investigate the theoretical question whether the standard deterministic linear program for network revenue management does provide an upper bound on the optimal expected revenue if customer's willingness to pay varies over time.


2021 ◽  
Author(s):  
Nick Arnosti ◽  
Tim Randolph

We analyze the parallel lottery that is used to allocate hunting permits in the state of Alaska. Each participant is given tickets to distribute among lotteries for different types of items. Participants who win multiple items receive their favorite, and new winners are drawn from the lotteries with unclaimed items. When supply is scarce, equilibrium outcomes of parallel lotteries approximate a competitive equilibrium from equal incomes (CEEI), which is Pareto efficient. When supply is moderate, parallel lotteries exhibit two sources of inefficiency. First, some agents may benefit from trading probability shares. Second, outcomes may be “wasteful”: agents may receive nothing even if acceptable items remain unallocated. We bound both sources of inefficiency and show that each is eliminated by giving applicants a suitable number of tickets k: trades are never beneficial when k = 1, and waste is eliminated as [Formula: see text]. In addition, we show that the wastefulness of the k-ticket parallel lottery has some benefits: agents with strong preferences may prefer parallel lottery outcomes to those of any nonwasteful envy-free mechanism. These agents prefer small values of k, while agents with weak preferences prefer large values of k. Together, these results suggest that the k-ticket parallel lottery performs well under most circumstances and may be suitable for other settings where items are rationed. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.


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